Assessment of dereverberation algorithms for large vocabulary speech recognition systems

The performance of large vocabulary recognition systems, for instance in a dictation application, typically deteriorates severely when used in a reverberant environment. This can be partially avoided by adding a dereverberation algorithm as a speech signal preprocessing step. The purpose of this paper is to compare the effect of different speech dereverberation algorithms on the performance of a recognition system. Experiments were conducted on the Wall Street Journal dictation benchmark. Reverberation was added to the clean acoustic data in the benchmark both by simulation and by re-recording the data in a reverberant room. Moreover additive noise was added to investigate its effect on the dereverberation algorithms. We found that dereverberation based on a delay-and-sum beamforming algorithm has the best performance of the investigated algorithms.